- Load the R packages we will use
Download \(C0_2\) emissions per capita from Our World in Data into the directory for this post.
Assign the location of the file to ‘file_csv’. The data should be in the same directory as this file
Read the data into R and assign it to ‘emissions’
file_csv <- here("_posts","2021-03-01-reading-and-writing-data","co-emissions-per-capita.csv")
emissions <- read_csv(file_csv)
- Show the first 10 rows (observations of) ‘emissions’
emissions# A tibble: 22,383 x 4 Entity Code Year `Per capita CO2 emissions` <chr> <chr> <dbl> <dbl> 1 Afghanistan AFG 1949 0.00191 2 Afghanistan AFG 1950 0.0109 3 Afghanistan AFG 1951 0.0117 4 Afghanistan AFG 1952 0.0115 5 Afghanistan AFG 1953 0.0132 6 Afghanistan AFG 1954 0.0130 7 Afghanistan AFG 1955 0.0186 8 Afghanistan AFG 1956 0.0218 9 Afghanistan AFG 1957 0.0343 10 Afghanistan AFG 1958 0.0380 # ... with 22,373 more rows- Start with ‘emissions’ data Then
- Use ‘clean_names’ from the janitor package to make the names easier to work with
- assign the output to ‘tidy_emissions’
- show the first 10 rows of ‘tidy_emissions’
tidy_emissions <- emissions %>% clean_names() tidy_emissions# A tibble: 22,383 x 4 entity code year per_capita_co2_emissions <chr> <chr> <dbl> <dbl> 1 Afghanistan AFG 1949 0.00191 2 Afghanistan AFG 1950 0.0109 3 Afghanistan AFG 1951 0.0117 4 Afghanistan AFG 1952 0.0115 5 Afghanistan AFG 1953 0.0132 6 Afghanistan AFG 1954 0.0130 7 Afghanistan AFG 1955 0.0186 8 Afghanistan AFG 1956 0.0218 9 Afghanistan AFG 1957 0.0343 10 Afghanistan AFG 1958 0.0380 # ... with 22,373 more rows- Start with the ‘tidy_emissions’ THEN
- use ‘filter’ to extract rows with ‘year == 2018’ THEN
- use ‘skim’ to calculate the descriptive statistics
Table 1: Data summary Name Piped data Number of rows 221 Number of columns 4 _______________________ Column type frequency: character 2 numeric 2 ________________________ Group variables None Variable type: character
skim_variable n_missing complete_rate min max empty n_unique whitespace entity 0 1.00 4 32 0 221 0 code 13 0.94 3 8 0 208 0 Variable type: numeric
skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist year 0 1 2018.00 0.00 2018.00 2018.00 2018.00 2018.00 2018.00 ▁▁▇▁▁ per_capita_co2_emissions 0 1 5.05 5.79 0.03 1.09 3.51 6.65 39.27 ▇▂▁▁▁ - 13 observations have a missing code. How are these observations different?
- start with tidy_emissions then extract rows with year == 2018 and are missing a code
Entities that are not countries do not have country codes. 8. Start with tidy_emissions THEN - Use ‘filter’ to extract rows with year == 2018 and without missing codes THEN - use ‘select’ to drop the ‘year’ variable THEN - use ‘rename’ to change the variable ‘entity’ to ‘country’ - assign the output to ‘emissions_2018’
# A tibble: 13 x 4 entity code year per_capita_co2_emissions <chr> <chr> <dbl> <dbl> 1 Africa <NA> 2018 1.13 2 Asia <NA> 2018 4.37 3 Asia (excl. China & India) <NA> 2018 4.14 4 EU-27 <NA> 2018 6.87 5 EU-28 <NA> 2018 6.71 6 Europe <NA> 2018 7.50 7 Europe (excl. EU-27) <NA> 2018 8.39 8 Europe (excl. EU-28) <NA> 2018 9.16 9 International transport <NA> 2018 4.62 10 North America <NA> 2018 11.4 11 North America (excl. USA) <NA> 2018 4.75 12 Oceania <NA> 2018 11.4 13 South America <NA> 2018 2.59- Which 15 countries have the highest ‘per_capita_co2_emissions’?
- start with ‘emissions_2018’ THEN
- use ‘slice_max’ to extract the 15 rows with the ‘per_capita_co2_emissions’
- assign the output to ‘max_15_emitters’
max_15_emitters <- emissions_2018 %>% slice_max(per_capita_co2_emissions, n=15)- Which 15 countries have the lowest ‘per_capita_co2_emissions’?
- start with ‘emissions_2018’ THEN
- use ‘slice_min’ to extract the 15 rows with the lowest values
- assign the output to ‘min_15_emitters’
min_15_emitters <- emissions_2018 %>% slice_min(per_capita_co2_emissions, n= 15)- Use ‘bind_rows’ to bind together the ‘max_15_emitters’ and ‘min_15_emitters’
- assign the output to ‘max_min_15’
max_min_15 <- bind_rows(max_15_emitters, min_15_emitters)- Export max_min_15 to 3 file formats
max_min_15 %>% write_csv("max_min_15.csv") max_min_15 %>% write_tsv("max_min_15.tsv") max_min_15 %>% write_delim("max_min_15.psv", delim = "|")- Read the 3 file formats into R
max_min_15_csv <- read_csv("max_min_15.csv") max_min_15_tsv <- read_tsv("max_min_15.tsv") max_min_15_psv <- read_delim("max_min_15.psv", delim = "|")- Use ‘setdiff’ to check for any differences among ‘max_min_15_csv’
setdiff(max_min_15_csv,max_min_15_tsv,max_min_15_psv)# A tibble: 0 x 3 # ... with 3 variables: country <chr>, code <chr>, # per_capita_co2_emissions <dbl>Are there any differences?
- Reorder ‘country’ in ‘max_min_15’ for plotting and assign to max_min_15_plot_data
- start with ‘emissions_2018’ THEN
- use ‘mutate’ to reorder ‘country’ according to ‘per_capital_co2_emissions’
max_min_15_plot_data <- max_min_15 %>% mutate(country = reorder(country, per_capita_co2_emissions))- Plot ‘max_min_15_plot_data’
ggplot(data = max_min_15_plot_data, mapping = aes(x=per_capita_co2_emissions,y= country)) + geom_col() + labs(title = "The top 15 and bottom 15 per capita CO2 emissions", subtitle = "for 2018", x= NULL, y= NULL)
- Save the plot directory with this post
ggsave(filename = "preview.png", path = here("_posts", "2021-03-01-reading-and-writing-data"))- Add preview.png to yaml chuck at the top of this file
preview: preview.png
- Save the plot directory with this post
- Plot ‘max_min_15_plot_data’